Efficient Decentralized Multi-task Dataset Valuation via Model Merging
Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models. Most existing valuation methods, however, are limited to single-task settings, overlooking scenarios where a buyer aims to optimize performance across multiple downstream tasks. Moreover, traditional valuation approaches, such as Shapley-based or retraining-based methods, are computationally expensive and poorly suited for decentralized environments without a trusted central coordinator and with strict privacy constraints. We propose DMVM (Decentralized Multi-task Valuation via Model Merging), a novel ...
| Authors | Mohammadsajad Alipour, Mohammad Mohammadi Amiri |
|---|---|
| Primary category | cs.CL |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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